Advanced Optimization Methods for The Design of Aluminium Based Battery Enclosures for Electric Vehicles

电动汽车铝基电池外壳设计的高级优化方法

• 批准号: 2891974
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891974
• 关键词: Advanced; Optimization; Methods; Design; Aluminium

DescriptionClimate change is one of the most important topics in the 21st century. The transportation sector, a major emitter of CO2, must adapt to reduce its impact on the environment. As a part of this sector, the automotive industry is focusing on electric vehicles as one of its strategies to reduce CO2 emissions. As the electric vehicle market grows, manufacturers are developing new vehicle platforms specifically for electric vehicles.One of the challenges is compensating for the added weight of batteries by decreasing vehicle body weight. This is especially important because the additional battery mass reduces the range of an electric vehicle. Thus, lightweight engineering is a crucial part of the vehicle's design, including the battery enclosure's design. Aluminium extrusion profiles for battery enclosures are one of the preferred structures subjected to crash loads, due to their high energy absorption to weight ratio.The design process of such battery enclosures is conducted and expedited using advanced computer-aided engineering tools, cutting down physical tests. Yet, the process still heavily relies on the designer's expertise. Advanced computer-aided engineering tools targeting optimised solutions, based on finer design details like material choice, can accelerate market entry. This, enabling designers to effectively design and optimize battery enclosures, considering material properties and various assembly methods such as welding, bonding, or mechanical fastening. Therefore, enabling lightweight structures that save material and resources. For this purpose, an adopted methodology must be developed.Aims and objectivesThe proposed research aims to develop an optimised design process for electric vehicle battery enclosures. The research is interdisciplinary bridging several disciplines, namely materials science, engineering science, and computer science.Focusing on critical aspects of materials engineering, impact and shock performance of material systems, and optimal design to enhance the topology of aluminium extrusion profile.Applying machine learning techniques for speed improvements of the structure's topology optimisation under consideration of the material properties and impact specific characteristics such as strain rate dependency.The proposed investigation will rely upon stochastic approaches to represent the mechanical performance of materials systems at multiple length scales. The proposed objectives help to enhance and accelerate the design process of battery enclosure structures.Novelty of the research methodologyPhysics-informed neural networks, capable of capturing the underlying mechanics of the problem, are often considered more advanced than purely data-driven methods. By incorporating properties like the material system, rate-dependent material behaviour, and other properties, these networks can notably speed up the topology optimization process while considering the physics. This enables faster optimisation and design of structures subjected to impact loads.Additionally, by conducting a variability analysis, we can gauge the impact of multiple structural properties, including the manufacturing process and rate-dependent material behaviour, on the structure's topology.The proposed methodology aims to expedite and improve the design of battery enclosure profiles while ensuring they meet crash safety requirements.EPSRCThis project falls within the EPSRC engineering design research area.Involved partnerThe EPSRC iCase involves the industrial partner Constellium.

描述气候变化是21世纪最重要的话题之一。交通运输业是二氧化碳的主要排放者，必须进行调整以减少其对环境的影响。作为该行业的一部分，汽车行业将电动汽车作为减少二氧化碳排放的战略之一。随着电动汽车市场的增长，制造商正在开发专门针对电动汽车的新汽车平台。挑战之一是通过减轻车身重量来补偿电池增加的重量。这一点尤其重要，因为额外的电池质量会减少电动汽车的行驶里程。因此，轻量化工程是车辆设计的关键部分，包括电池外壳的设计。用于电池外壳的铝挤压型材因其高能量吸收与重量比而成为承受碰撞载荷的首选结构之一。此类电池外壳的设计过程是使用先进的计算机辅助工程工具进行和加速的，减少了物理测试。然而，这个过程仍然在很大程度上依赖于设计师的专业知识。先进的计算机辅助工程工具针对优化的解决方案，基于材料选择等更精细的设计细节，可以加速市场进入。这使得设计人员能够有效地设计和优化电池外壳，同时考虑材料特性和各种组装方法，例如焊接、粘合或机械紧固。因此，可以实现节省材料和资源的轻质结构。为此，必须开发一种采用的方法。目的和目标拟议的研究旨在开发电动汽车电池外壳的优化设计流程。该研究是跨学科的桥梁，即材料科学、工程科学和计算机科学。重点关注材料工程的关键方面、材料系统的冲击和冲击性能，以及增强铝挤压型材拓扑的优化设计。在考虑材料特性和影响应变率依赖性等具体特征的情况下，应用机器学习技术来加快结构拓扑优化的速度。所提出的研究将依赖于 表示材料系统在多个长度尺度上的机械性能的随机方法。所提出的目标有助于增强和加速电池外壳结构的设计过程。研究方法的新颖性基于物理的神经网络能够捕获问题的根本机制，通常被认为比纯粹的数据驱动方法更先进。通过结合材料系统、速率相关的材料行为和其他属性等属性，这些网络可以在考虑物理因素的同时显着加快拓扑优化过程。这使得能够更快地优化和设计承受冲击载荷的结构。此外，通过进行变异性分析，我们可以衡量多种结构特性（包括制造工艺和速率相关材料行为）对结构拓扑的影响。所提出的方法旨在加快和改进电池外壳型材的设计，同时确保它们满足碰撞安全要求。EPSRC该项目属于 EPSRC 工程设计研究范围 相关合作伙伴 EPSRC iCase 涉及工业合作伙伴 Constellium。